Study of expression levels of 14 genes in human brain autopsy and biopsy samples found significant change in one of the genes, indicating that a substantial proportion of all expressed g
Trang 1Systematic analysis of gene expression in human brains before and
after death
Henriette Franz * , Claudia Ullmann † , Albert Becker † , Margaret Ryan ‡ ,
Sabine Bahn ‡ , Thomas Arendt § , Matthias Simon ¶ , Svante Pääbo * and
Philipp Khaitovich *
Addresses: * Max-Planck-Institute for Evolutionary Anthropology, Deutscher Platz, D-04103 Leipzig, Germany † Department of
Neuropathology and National Brain Tumor Reference Center, University of Bonn Medical Center, Sigmund-Freud-Strasse, D-53105 Bonn,
Germany ‡ Cambridge Centre for Neuropsychiatric Research, Institute of Biotechnology, University of Cambridge, Tennis Court Road,
Cambridge CB2 1QT, UK § Paul Flechsig Institute for Brain Research, University of Leipzig, Jahnallee, D-04109 Leipzig, Germany ¶ Department
of Neurosurgery, University of Bonn Medical Center, Sigmund-Freud-Strasse, D-53105 Bonn, Germany
Correspondence: Philipp Khaitovich E-mail: khaitovich@eva.mpg.de
© 2005 Franz et al.; licensee BioMed Central Ltd
This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which
permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Profiling post-mortem human brains
<p>Comparison of the gene expression profiles of pre- and post-mortem human brains suggests that post-mortem human brain samples
are suitable for investigating general gene-expression patterns.</p>
Abstract
Background: Numerous studies have employed microarray techniques to study changes in gene
expression in connection with human disease, aging and evolution The vast majority of human
samples available for research are obtained from deceased individuals This raises questions about
how well gene expression patterns in such samples reflect those of living individuals
Results: Here, we compare gene expression patterns in two human brain regions in postmortem
samples and in material collected during surgical intervention We find that death induces significant
expression changes in more than 10% of all expressed genes These changes are non-randomly
distributed with respect to their function Moreover, we observe similar expression changes due
to death in two distinct brain regions Consequently, the pattern of gene expression differences
between the two brain regions is largely unaffected by death, although the magnitude of differences
is reduced by 50% in postmortem samples Furthermore, death-induced changes do not contribute
significantly to gene expression variation among postmortem human brain samples
Conclusion: We conclude that postmortem human brain samples are suitable for investigating
gene expression patterns in humans, but that caution is warranted in interpreting results for
individual genes
Background
Microarray studies examining gene expression profiles of
thousands of genes have become an important tool in
uncov-ering molecular mechanisms of human diseases, aging and
evolution [1-3] Many such studies are conducted on
post-mortem human tissues, since neither cell culture nor animal models can fully recapitulate relevant human conditions [4,5] This is particularly the case for studies that examine the human brain Several factors may alter gene expression pro-files in postmortem human brain samples Such factors
Published: 30 December 2005
Genome Biology 2005, 6:R112 (doi:10.1186/gb-2005-6-13-r112)
Received: 4 July 2005 Revised: 23 August 2005 Accepted: 6 December 2005 The electronic version of this article is the complete one and can be
found online at http://genomebiology.com/2005/6/13/R112
Trang 2include the delay between death and the time of tissue
freez-ing, the method of freezfreez-ing, and the duration of storage of the
frozen brain material Prior studies have indicated that these
factors have relatively small effects on gene expression [6-8]
In contrast, the duration and nature of the agonal state
pre-ceding death appear to have a substantial effect on gene
expression by affecting the integrity of messenger RNAs
[7-9] Thus, postmortem brain samples obtained from
individu-als who died after a protracted agonal phase are not suitable
for gene expression studies Without any prolonged agonal
conditions, however, death itself may alter gene expression
patterns in postmortem human brains Study of expression
levels of 14 genes in human brain autopsy and biopsy samples
found significant change in one of the genes, indicating that a
substantial proportion of all expressed genes could be
affected by death [10]
We surveyed gene expression in 10 postmortem human brain
samples (autopsy samples) and 12 samples obtained from
brain surgery (resection samples) derived from frontal cortex
and hippocampus using Affymetrix® HG-U133plus2
microar-rays containing probes for all annotated human genes All
autopsy samples were obtained from individuals that died
rapidly with no prolonged agonal state, thus minimizing the
influence of agonal factors on gene expression patterns in our
study
Results Expression differences between autopsy and resection samples
Gene expression profiles were determined in six resection samples from hippocampus and frontal cortex, and in four and six autopsy samples from hippocampus and frontal cor-tex, respectively, using Affymetrix® HG U133plus2 arrays (see Materials and methods) Of the 54,613 probe sets on the microarray, 42,427 (77.69%) gave a detectable hybridization signal in at least one individual (see Materials and methods) Among these probe sets, we found 5,703 with a significant dif-ference in expression (13.4%) using analysis of variance (ANOVA) with a nominal significance cutoff of 0.01 (false dis-covery rate (FDR) = 4.12%, permutation test) and 8,643 using significance analysis of microarrays (SAM) at the 5% FDR cutoff Out of the 5,703 probe sets identified in ANOVA, 5,515 (96.7%) overlapped with the probe sets identified by SAM Further, of these 5,703 probe sets, 4,508 differed significantly
(p < 0.01) between autopsy and resection samples in both
brain regions while 981 probe sets showed a significant differ-ence between autopsy and resection samples as well as between brain regions (Figure 1) For none of these 5,489 probe sets did the differences between autopsy and resection samples depend significantly on the brain region Finally, for
214 probe sets (0.5% of all detected ones), expression differ-ences between autopsy and resection samples differed
signif-icantly (p < 0.01) depending on the brain region examined.
This indicates that death-induced expression changes are highly consistent in both brain regions and influence only a small fraction of the total observed expression differences (214 out of 5,703)
Since all but one surgery patient were diagnosed with epilepsy (Table 1), we first tested whether differences between autopsy and resection samples are significantly affected by the epilep-tic condition Among the 42,427 expressed probe sets, we found none with a significant effect of epilepsy either in hip-pocampus or in frontal cortex using both linear regression and SAM (FDR = 5.0%) Further, we tested whether known changes in expression caused by epilepsy are over-repre-sented among differences seen between autopsy and resec-tion samples Using a published set of genes where expression
change was observed in at least two epilepsy studies (N = 54)
[11], we found no such over-representation (Fisher's exact
test, p = 0.45) Finally, we tested whether expression
differ-ences we found between autopsy and resection are also seen when only the samples unaffected by epilepsy are considered
To this end, we identified probe sets showing expression dif-ferences between autopsy and resection samples, excluding from the analysis samples from patients not affected by
epi-lepsy (ANOVA, p < 0.01) We found a strong and significant
correlation when these expression differences were compared
to the ones observed in non-affected control samples; three resections composed of two cerebral cortex samples from an unaffected region and one hippocampus sample from a
non-epileptic patient gave Pearson's correlation R = 0.948 (N =
ANOVA test results
Figure 1
ANOVA test results Numbers indicate number of probe sets with
expression significantly influenced by brain region, source of sample
material, and their interaction The interaction term is significant when the
expression changes due to death differ significantly in the two brain
regions examined (see Material and methods) Numbers in brackets
indicate the percentage of significant probe sets compared to the total
number included in the analysis Overlapping regions include probe sets
with more than one significant term.
Region
5353
(12.6%)
Source 4508 (10.6%)
Source•region 383
(0.9%)
981 (2.3%)
128 (0.3%)
108 (0.25%) 106
(0.25%)
Trang 32,983, p < 10-15) or using the one hippocampus sample only
gave Pearson's correlation R = 0.905 (N = 4,088, p < 10-15)
Thus, the overwhelming majority of expression differences
between autopsy and resection identified in samples affected
by epileptic condition are also present in the non-affected
samples
We next asked whether the genes represented by the 4,508
probe sets that showed significant differences in expression
between autopsy and resection samples in both brain regions
cluster in functional categories as defined by the Gene
Ontol-ogy (GO) consortium [12] Differently expressed genes
clus-tered significantly in all three GO taxonomies, 'biological
process', 'molecular function' and 'cellular component' (p <
0.0001) Among 15 GO 'biological process' categories with
significant over-representation of differently expressed
genes, four are involved in cellular protein metabolism and
six in nucleobase, nucleoside, nucleotide and nucleic acid
metabolism Most of the remaining genes are found in the
categories 'organelle organization and biogenesis' and
'intra-cellular protein transport' (Table 2) The expression of genes involved in the ubiquitin cycle and protein ubiquitination is significantly increased after death, while the expression of genes involved in protein biosynthesis, rRNA processing, organelle organization and biogenesis and induction of
apop-tosis are significantly decreased (two-sided binomial test, p <
0.05)
Among 20 GO categories with significant under-representa-tion of genes differently expressed between autopsies and resections, seven are involved in cell communication, three in response to stimulus, two in sensory perception, and four in development In addition, 'cellular physiological process' and 'organismal physiological process' are among the GO catego-ries that are significantly conserved in their expression between autopsy and resection samples (Table 2)
In contrast, no chromosome showed either an excess or lack
of expression differences (two-sided binomial test, p < 0.341,
corrected for multiple testing)
Table 1
Sample information
Sample* Age
(years)
Sex 28S/18S ratio†
GAPDH 5'/3' ratio‡
Expressed probe sets (%)§
Diagnosis Epilepsy Types of seizures
-HR1 45 M 1.1 0.520 50.5 Anaplastisches Oligo WHO III Yes Simple partial
HR2 39 F 1.3 0.700 50.2 Glioblastoma Yes Simple and complex partial, GM
HR3 61 M 1.6 0.774 53.8 Glioblastoma Yes Simple and complex partial
HR4 51 F 1.6 0.697 49.5 Ammon's horn sclerosis Yes Simple and complex partial, GM
HR5 13 M 1.4 0.778 47.1 Ganglioglioma Yes Complex partial
HR6 83 F 1.3 0.817 50.0 Atpisches Meningeom Grad II No
-CR1 35 F 1.2 0.741 45.9 Focal cortical dysplasia Yes Complex partial, GM
CR2 31 F 1.3 0.741 39.5 Focal cortical dysplasia Yes Simple partial
CR3 9 F NA 0.607 45.6 Focal cortical dysplasia Yes Complex partial
CR4 37 M NA 0.674 43.7 Focal cortical dysplasia Yes Complex partial
CR5 35 F NA 0.737 48.8 Focal cortical dysplasia Yes Complex partial, GM
CR6 31 F NA 0.674 43.1 Focal cortical dysplasia Yes Simple partial
*Sample names: position one = brain region (H, hippocampus; C, cortex); position two = sample source (A, autopsy; R, resection); position three =
individual †Ribosomal RNA bands ratio was measured using Agilent 2100 Bionalyzer system ‡GAPDH ratio was measured using probes to 5' and 3'
of the transcript on Affymetrix® array §Expressed probesets were defined based on detection p < 0.05 F, female; GM, grand mal; M, male; NA, not
applicable
Trang 4Expression differences between brain regions
To test whether in vivo expression differences between the
brain regions are conserved in postmortem samples, we first
considered the ANOVA results (Figure 1) Among 42,427
probe sets with hybridization signals detectable in at least one
individual, 6,568 (15.5%) showed significant expression
dif-ferences between the two brain regions analyzed (nominal
significance p < 0.01, FDR = 3.6%, permutation test) Out of
these probe sets, 6,431 (97.9%) overlapped with the ones identified by SAM (FDR = 5%) In 234 of these 6,431 probe sets, differences between brain regions depended
signifi-cantly on the source of sample material (p < 0.01) Thus,
although autopsy and resection samples differ substantially with regard to their gene expression profiles, the patterns of expression differences between the brain regions remain largely preserved
Table 2
Functional analysis of gene expression differences between autopsy and resection samples
GO ID Term Expressed genes Significant differences* Change p value Conservation p value
GO:0006412 Protein biosynthesis 462 101 (37/64) 0.001 0.999 GO:0006512 Ubiquitin cycle 473 119 (86/33) 0.000 1.000 GO:0016567 Protein ubiquitination 256 60 (41/19) 0.002 0.999 GO:0006511 Ubiquitin-dependent protein catabolism 104 36 (23/13) 0.000 1.000 GO:0006396 RNA processing 341 118 (64/54) 0.011 0.995 GO:0006397 mRNA processing 217 74 (44/30) 0.002 0.999 GO:0008380 RNA splicing 183 67 (39/28) 0.000 1.000 GO:0006281 DNA repair 168 40 (23/17) 0.009 0.995 GO:0000398 Nuclear mRNA splicing, via spliceosome 155 54 (30/24) 0.000 1.000 GO:0006364 rRNA processing 32 16 (3/13) 0.000 1.000 GO:0006996 Organelle organization and biogenesis 367 83 (30/53) 0.048 0.964 GO:0006886 Intracellular protein transport 263 62 (32/30) 0.002 0.999 GO:0008624 Induction of apoptosis by extracellular signals 28 13 (2/11) 0.000 1.000 GO:0006120 Electron transport, NADH to ubiquinone 24 10 (3/7) 0.003 0.999 GO:0048247 Lymphocyte chemotaxis 3 3 (0/3) 0.004 1.000 GO:0007242 Intracellular signaling cascade 879 105 0.989 0.016 GO:0007186 GPCR protein signaling pathway 448 39 1.000 0.000 GO:0007267 Cell-cell signaling 417 39 0.998 0.003 GO:0007243 Protein kinase cascade 231 24 0.997 0.005 GO:0045860 Positive regulation of protein kinase activity 41 1 0.999 0.006 GO:0007268 Synaptic transmission 203 18 0.999 0.001 GO:0007187 G-protein signaling (cyclic nucleotide second
messenger)
GO:0050896 Response to stimulus 1,326 179 0.975 0.035 GO:0009605 Response to external stimulus 781 90 0.972 0.037 GO:0009617 Response to bacteria 37 0 1.000 0.001 GO:0007601 Visual perception 126 9 0.999 0.002 GO:0007606 Sensory perception of chemical stimulus 55 2 0.999 0.003 GO:0007275 Development 1,412 174 0.992 0.011 GO:0009887 Organogenesis 770 89 0.997 0.004 GO:0007417 Central nervous system development 92 6 0.999 0.004 GO:0008544 Epidermis development 39 1 0.999 0.008 GO:0050875 Cellular physiological process 3,372 515 1.000 0.000 GO:0050874 Organismal physiological process 1,200 138 0.997 0.004 GO:0006813 Potassium ion transport 139 3 1.000 0.000 GO:0030003 Cation homeostasis 52 1 1.000 0.001
*Numbers in parenthesis correspond to the number of up- and down-regulated genes in the autopsy samples Bold font indicates Gene Ontology (GO) groups with significant excess of up- or down-regulated genes (see Materials and methods)
Trang 5We tested further whether in vivo expression differences
between the brain regions are conserved in the postmortem
samples by separately identifying, independent of the
ANOVA results, probe sets differently expressed between the
brain regions in the autopsy and in the resection samples
Using Student's t test with nominal significance p < 0.01, we
found 788 and 3,943 probe sets with a significant difference
in expression between the brain regions in the autopsy and in
the resection samples, respectively (FDR = 22.8% and 4.3%
respectively, permutation test) Similarly, using SAM with
FDR = 5% we found 874 and 6,699 probe sets with a
signifi-cant difference in expression between the brain regions in the
autopsy and in the resection samples, respectively This large
discrepancy in the numbers of differences between the brain
regions when the autopsy and resection samples are
consid-ered separately seems to contradict the ANOVA results To
address this, we examined whether probe sets that do not
overlap between these two lists tend to show the same pattern
of change between the brain regions or, alternatively, are
completely uncorrelated in their expression behavior For
this purpose, we considered all probe sets present on either of
the two lists and calculated the strength of correlation of the
expression difference between the brain regions measured in
the autopsy and in the resection samples We found a strong
and significant correlation between the expression
differ-ences for both t test (Pearson's correlation R = 0.763, N =
4,471, p < 10-15) and SAM results (Pearson's correlation R =
0.726, N = 7,162, p < 10-15) (Figure 2) Similarly, we found
slightly reduced but still highly significant correlations using
expression differences normalized to the average variation
(effect size) (Pearson's correlation R = 0.566, p < 10-15 and R
= 0.584, p < 10-15, respectively) Thus, expression differences
betweenthe two brain regions are largely concordant in the
autopsy and resection samples Interestingly, the slopes of the
regression lines (β) fitted through the distributions of the
expression differences between the two brain regions in the
autopsy and the resection samples equal 0.49 for both sets of
genes (Figure 2) An even stronger effect was observed using the effect size measurements (β = 0.33 and β = 0.32 for t test
and SAM results, respectively) Thus, despite an overall agreement of the measurements of expression differences in the two sources of sample material, the amplitude of expres-sion differences measured in the autopsy samples is, on aver-age, half of that observed in the resection samples Limiting the regression to genes with a high expression difference amplitude in either autopsy or resection samples did not change this effect Interestingly, it was even more pro-nounced for genes with lower expression in the frontal cortex compared to the hippocampus (β = 0.27 and β = 0.34 for t test
and SAM results, respectively) Since the significance test depends on the effect size, smaller expression differences explain the reduced number of identified probe sets in the autopsy samples
Influence of death on expression variation
All microarray studies involving postmortem human samples report substantial biological variation among individuals We asked whether death-induced expression changes contribute
to this variation by affecting different individuals to different degrees To do this, we examined published gene expression data from 40 brain autopsy samples [13] First, we asked whether probe sets that differ in expression between autopsy and resection samples vary more among individuals in this dataset than other probe sets From the 16,376 probe sets with a detectable hybridization signal in at least one of the 40 individuals, 1,752 overlap with the probe sets showing signif-icant differences in expression between autopsy and resection samples Using logarithm transformed variation measures,
we found no significant difference between the expression variation among these probe sets and among the remaining
probe sets (Student's t test, p = 0.916) Thus, genes that differ
in expression between autopsy and resection samples do not vary more among postmortem samples compared to the other genes
Next, we asked whether the amplitude of death-induced expression changes correlates with the duration of postmor-tem interval To test this, we computed correlations between gene expression levels and postmortem delay in the 40 brain autopsy samples for 1,752 probe sets that differ in expression between autopsy and resection samples and for 1,000 subsets
of the same size randomly sampled from the other 14,624 probe sets In 837 out of 1,000 random subsets, the correla-tion was greater or equal to the one observed for probe sets with significant difference in expression between autopsy and resection samples Thus, genes that differ in expression between autopsy and resection samples do not correlate more with duration of postmortem interval than the rest of the detected genes
Scatter plot of expression differences between cortex and hippocampus in
resection (x-axis) and autopsy (y-axis) samples
Figure 2
Scatter plot of expression differences between cortex and hippocampus in
resection (x-axis) and autopsy (y-axis) samples Expression differences
were calculated as base two logarithm transformed ratios of gene
expression values All probe sets showing significant differences in
expression levels between the two brain regions, either in the autopsy or
in resection samples, are plotted: (a) according to Student's t test; (b)
according to SAM Red dashed lines represent linear regression results
and black dotted lines represent expected regression lines with the slope
= 1.
Resection
Resection
Trang 6In this study, we observe that death causes substantial
changes in the expression of more than 10% of genes
expressed in human brain Furthermore, this change is highly
reproducible, with 96% of differences being shared when two
very different brain regions (frontal cortex and hippocampus)
are considered Since all brain resection samples were
obtained from people with certain brain abnormalities, an
alternative explanation is that the observed changes are
induced by disease of the living brain rather than by death
However, for several reasons we find this explanation
unlikely First, we used resection samples from patients
suf-fering from several different neurological disorders (Table 1),
which are not likely to induce the same pattern of gene
expression change Second, although all but one of the
patients were diagnosed with epilepsy, severity of the disease
did not significantly influence expression differences between
autopsy and resection samples Third, we observed similar
gene expression differences between autopsy and resection
samples in both frontal cortex and hippocampus It is unlikely
that these brain regions are affected in the same way by the
diseases in question Finally, we found consistent gene
expression differences in the four frontal cortex samples
affected by disease at the histological level and the ones with
normal histology Taken together, these arguments suggest
that the gene expression differences we observed between
autopsy and resection samples are not due to disease-induced
change in the resection samples
Still, two factors, epilepsy and surgery, are shared among
most or all patients, respectively We found no genes with a
significant effect of epilepsy on expression either in
hippoc-ampus or in frontal cortex Similarly, using data from the
resection samples of non-epileptic patients, we found the
same expression differences between autopsy and resection
samples as we found with epileptic patients' samples In
addi-tion, known expression changes induced by epilepsy are not
over-represented among differences between autopsy and
resection samples These results indicate that epilepsy is
unlikely to have contributed a great deal to the expression
dif-ferences we see Due to the small number of samples used in
the analysis, however, we cannot completely exclude such an
effect Similarly, we cannot exclude influence of surgery and
surgery related treatments, like anesthesia, on gene
expres-sion in all resection samples This remains a confounding
fac-tor for estimation of the expression differences between
postmortem and living human brain tissue that we cannot
address in this study
Yet, given the widespread use of postmortem human brain
tissue in research, the most important question is how well
gene expression differences measured in postmortem
sam-ples reflect those occurring in vivo We found that despite the
large impact that death as such and, potentially, surgery have
on gene expression patterns in autopsy and resection
sam-ples, respectively, differences between brain regions that exist
in the living brain are mostly retained in postmortem sam-ples However, it is striking that the magnitude of the expres-sion differences between the two brain regions decreases by approximately 50% on average and that the effect size is reduced by approximately two-thirds in postmortem sam-ples This reduction did not depend on the magnitude of dif-ference Interestingly, the reduction was even more pronounced in genes with lower expression in frontal cortex than in hippocampus (Figure 2) This indicates that gene expression differences measured in postmortem brain sam-ples may underestimate differences existing in the living tissue
Interestingly, gene expression changes induced by death do not appear to increase variation among postmortem brain samples In agreement with this, we found no significant cor-relation between the duration of postmortem interval and the magnitude of expression differences between autopsy and postmortem samples This suggests that expression changes occur quickly in the process of dying and remain stable there-after This observation is in agreement with recent findings that postmortem delay does not substantially influence gene expression variation among human brain samples [6-8], whereas prolonged agonal states significantly influence expression profiles
The genes that differ in their expression between autopsy and resection samples are significantly over- and under-repre-sented in certain functional processes Genes involved in rather basic functions, such as RNA processing, protein bio-synthesis and transport, organelle organization and biogen-esis, the ubiquitin cycle, and DNA repair (Table 1) are over-represented among genes differently expressed between autopsies and resections We would have expected an overall down-regulation of these pathways in tissues after death Indeed, genes involved in rRNA processing, protein biosyn-thesis, induction of apoptosis, and organelle organization and biogenesis show significant down-regulation in the autopsy samples Interestingly, we also see up-regulation of genes involved in the ubiquitin cycle, protein ubiquitination, and ubiquitin-dependent protein catabolism This implies that death leads to the temporary induction of expression for some functional processes It is intriguing to think that death does not lead to immediate shut down of all functional processes
on a cellular level If these transcripts become translated to functional proteins, up-regulation of genes involved in ubiq-uitin-dependent protein catabolism may lead to increased degradation of proteins in human brain samples after death This could have consequences for protein studies in postmor-tem human brain samples, where protein degradation is com-monly observed [14-16] It may thus be important to compare protein patterns in postmortem andresection samples of human brains to estimate the extent of death-induced protein degradation
Trang 7More than three quarters of the GO categories with
signifi-cant conservation of their expression levels after death fall
into processes involved in intra- and extracellular signaling
and in development (Table 1) This is rather unexpected since
these processes underlie essential brain functions and genes
involved in such functions have been shown to differ in their
expression levels among various brain regions [17,18]
Intui-tively, one might expect that death would affect these
proc-esses first The excess or paucity of expression differences in
certain functional processes could be caused by differences in
RNA degradation rates In this case we would expect genes
with low RNA turnover to fall into functional categories that
maintain their observed expression levels after death and
genes with high RNA turnover to fall into significantly
changed functional categories However, genes involved in
signal transduction and development are known to have high
RNA turnover rates [19,20] while genes involved in general
metabolic functions, biosynthesis and catabolism have low
RNA turnover rates [20,21] Thus, it is unlikely that the
observed clustering of expression differences in distinct
func-tional categories is due to differences in RNA degradation
rates
Conclusion
Despite the large effect of death on gene expression in human
brain, postmortem samples maintain the vast majority of the
expression differences that exist between brain regions in
vivo However, the amplitude of expression differences
between brain regions in postmortem samples is reduced by
approximately 50% compared to the living tissue It should be
noted that the results reported here examined only a limited
number of samples representing only few conditions and that
confounding effects, including surgery and anesthesia, may
influence some of the expression differences we observe
Nev-ertheless, given that the primary source of brain tissue is
post-mortem collection, it is encouraging that there is such a high
degree of correlation in gene expression patterns between
sources
Materials and methods
Tissue samples and microarray data collection
Human postmortem samples were obtained from the
National Disease Research Interchange Informed consent
for use of the tissues for research was obtained in writing
from all donors or the next of kin None of the subjects had a
history of neurological disease or had indications of brain
abnormalities at the tissue level as determined at autopsy All
individuals suffered sudden death for reasons other than
their participation in this study and without any relation to
the tissues used Human resection samples were obtained
from patients with brain tumors and/or chronic
pharmaco-resistant epilepsy who underwent surgical treatment in the
Surgery/Epilepsy Surgery Programs at the University of Bonn
Medical Center In all patients, surgical removal of the
tumor/lesion tissue was necessary Informed consent for additional studies was obtained in writing from all patients
The diagnosis of the individual patients is presented in Table
1 All procedures were conducted in accordance with the Dec-laration of Helsinki and approved by the ethics committees of the respective institutions Representative tissue sections were snap frozen at -80°C Based on neuropathological anal-yses by means of hematoxilin and eosin stainings, normal tis-sue adjacent to the tumor or lesions was used for subsequent experiments Intense care was taken to avoid tumor infil-trated tissue None of the surgically obtained tissue samples used in this study, with the exception of four frontal cortex samples with focal cortical dysplasia, showed any histological abnormalities Age, sex, and degree of relatedness of all indi-viduals are listed in Table 1
All samples were processed in parallel starting from the fro-zen tissue by the same person (HF) in random order with respect to brain region and the source of sample material
Total RNA was isolated from approximately 50 mg of frozen tissue using TRIZol® (GIBCO, San Diego, CA, USA) reagent according to the manufacturer's instructions and purified with QIAGEN® RNeasy® kit (Valencis, CA, USA) following the 'RNA cleanup' protocol All RNA samples were of high and comparable quality as determined by the ratio of 28S to 18S ribosomal RNAs estimated using the Agilent® (Palo Alto, CA, USA) 2100 Bioanalyser® system and by the signal ratios between the probes for the 5' and 3' ends of the mRNAs of GAPDH used as quality controls on Affymetrix® (Santa Clara, CA< USA) microarrays (Table 1) Labeling of 1.2 µg of total RNA, hybridization to Affymetrix® HG U133plus2 arrays, staining, washing and array scanning were carried out follow-ing Affymetrix® protocols All primary expression data are publicly available at the ArrayExpress database (accession number E-TABM-20) [22]
Microarray data analyses
Affymetrix® microarray image data were collected with Affymetrix® GeneChip® Operating Software version 1.1 using default parameters We used the robust multichip average (rma) procedure [23] for array normalization and calculation
of expression base two logarithm transformed intensity val-ues Since logarithm-transformed intensity values are approximately normally distributed, we used them for all
analyses We calculated detection p values using the
Biocon-ductor 'affy' software package [24] We defined probe sets having a detectable hybridization signal using Affymetrix default detection cutoff of 0.065
We used ANOVA to identify probe sets that showed a statisti-cally significant change in expression depending on the brain region or on the source of sample material among human
samples using the following model: Y ij = µj + sourcei + regioni + (source*region)i + εij In this equation, Yij is the base two
logarithm of the expression level for probe set j in sample i, µ
is the mean expression level of a probe set j, source i is the term
Trang 8for the effect of the source of sample material, regioni is the
term for the effect of the source of the brain region,
(source*region)i is the term for the interaction effect of the
two factors, and εij is the error term For each term we used a
nominal significance level of 0.01 In order to estimate an
average number of probe sets expected by chance at this
sig-nificance cutoff, we applied the same ANOVA approach to
1,000 datasets constructed by random permutation of the
sample labels in the original data
Alternatively, differently expressed probe sets were
deter-mined using SAM software version 2.01 with 5% FDR cutoff
[25] In all cases except the analysis of epilepsy effects, we
performed t statistics on the logarithm transformed
expres-sion values FDR estimates were based on 500 permutations
of the samples within the set We used block permutation
design for the two-factor analysis and time course for the
analysis of epilepsy effects Effect of epilepsy was scored
based on the diagnosis and seizure type: 0, no diagnosed
epi-lepsy; 1, simple partial seizures; 2, simple and complex partial
seizures; 3, complex partial seizures; 4, simple and complex
partial seizures, grand mal; 5, complex partial seizures Effect
size was calculated as a difference between means divided by
the pooled standard deviation The pooled standard deviation
was defined as the square root of the average of the squared
standard deviations
Functional analysis and distribution on chromosomes
To functionally annotate the probe sets on the Affymetrix®
HG U133plus2 arrays, we integrated information from four
public databases: Affymetrix® NetAffx™ (12/2004 release)
[26], LocusLink (12/2004 release) [27], and Gene Ontology
(12/2004 release) [28] Affymetrix® probe sets were linked to
the corresponding genes using LocusLink annotation
pro-vided by NetAffx™ When a single gene was represented by
multiple probe sets, the gene was classified as detected if at
least one probe set was detected and classified as
differen-tially expressed if at least one probe set was both detected and
differentially expressed Genes were assigned to their GO
annotations from each of the three GO taxonomies
('molecu-lar function', 'biological process', and 'cellu('molecu-lar component')
using GenMapper [29,30] Note that a gene belongs to its
assigned GO group as well as all higher groups in the
taxonomy
To assess if the overall distribution of genes differentially
expressed between autopsy and resection samples across the
groups in a GO taxonomy differs significantly from the
distri-bution of all detected genes, we compared it with 10,000
ran-dom sets in which the same number of differentially
expressed genes was randomly drawn from the annotated
detected genes as described elsewhere [18] GO groups with
significant excess and with significant lack of expression
dif-ferences between autopsy and resection samples were
deter-mined independently using the hypergeometric distribution
[18] The percentage of false positive GO groups was
esti-mated from the ratio of the number of significant groups in the observed data to the average number of the significant groups in 10,000 random sets In the GO taxonomy 'biologi-cal process', we expect 20% false positives for the groups with significant excess and 5.8% false positives for the groups with significant lack of expression differences between autopsy and resection samples Significant over-representation of
up-or down-regulated genes in GO groups with significant excess
of expression differences was determined by binomial test Probability of up- and down-regulation within a group was based on distribution of all differently expressed genes To assign chromosomal location to genes we used annotation provided by NetAffx™ Genes differently expressed between autopsy and resection samples were defined the same way as for the functional analysis
Acknowledgements
We thank Stanley Medical Research Institute, Bethesda, for providing the well-matched brain collection courtesy of MB Knable, EF Torrey, MJ Web-ster, S Weis and RH Yolken; U Gärtner of the Paul Flechsig Institute, Leip-zig, for help with dissections; M Lachmann, W Enard, J Kelso, M Leinweber, and all members of our laboratory for discussion; H Creely for critical read-ing of the manuscript; the Max Planck Society, the Bundesministerium für Bildung und Forschung grant 01GR0481, and the Sächsisches Staatsministe-rium für Wissenschaft und Kunst for financial support.
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